Mri Analyzer
Mri Analyzer - Related Videos
06:06
Optogenetic Functional MRI
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Cited by 15 •
2016
This protocol describes the steps and data analysis required to successfully perform optogenetic functional magnetic resonance imaging (ofMRI). ofMRI is a novel technique that combines high-field fMRI readout with optogenetic stimulation, allowing for cell type-specific mapping of functional neural circuits and their dynamics across the whole living brain.
06:58
Synthesis of an In vivo MRI-detectable Apoptosis Probe
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Cited by 3 •
2012
Early detection of apoptosis may identify at-risk cell populations in a variety of diseases. Here we demonstrate a method to link an early apoptosis-detection protein (Annexin V) to a MRI-detectable iron oxide nanoparticle (SPIO). This method may be extended to other proteins of interest to generate MRI-detectable molecular imaging probes.
16:20
Hyperpolarized Xenon for NMR and MRI Applications
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Cited by 28 •
2012
The production of hyperpolarized xenon by means of spin exchange optical pumping (SEOP) is described. This method yields a ~10000-fold enhancement of the nuclear spin polarization of Xe-129 and has applications in nuclear magnetic resonance spectroscopy and imaging. Examples of gas phase and solution state experiments are given.
08:20
MRI-guided dmPFC-rTMS as a Treatment for Treatment-resistant Major Depressive Disorder
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Cited by 31 •
2015
Here we outline the procedure for MRI-guided repetitive transcranial magnetic stimulation to the dorsomedial prefrontal cortex as an experimental treatment for major depressive disorder.
12:27
Noninvasive In Vivo Small Animal MRI and MRS: Basic Experimental Procedures
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Cited by 9 •
2009
This work describes basic procedures of noninvasive small animal MRI and MRS in vivo.
FAQs
Each processing stage addresses a different source of uncertainty. Image reconstruction produces an interpretable image, noise reduction limits unwanted variation, and registration aligns images from different samples or time points. Together, these operations make anatomical regions easier to compare and help distinguish meaningful biological changes from differences caused by image quality or positioning.
Segmentation separates an image into biologically relevant regions, such as distinct tissue structures or areas that change during an experiment. Once regions are identified, the workflow can calculate quantitative measurements for each one rather than relying only on visual inspection. This supports reproducible comparisons of tissue organization, composition, or function across samples and conditions.
After images from multiple time points are registered, corresponding regions can be examined using the same measurement approach. Researchers can then compare tissue structure, composition, or function within an individual sample or across samples. This longitudinal perspective helps identify developmental progression, disease-related change, or responses to experimental treatments while preserving the ability to study the same subject repeatedly.
A basic workflow begins with image reconstruction and preparation of the MRI data, followed by noise reduction to improve consistency. Registration aligns images when comparisons involve different samples or time points. Segmentation then identifies relevant regions, and quantitative measurements summarize their properties. The resulting values can be compared across regions, experimental groups, or stages of a study.
Researchers can use this approach when they need biological information from tissue without destructive sampling. It is particularly relevant to studies of brain organization, development, disease-related tissue changes, and responses to experimental treatments. Because image-based measurements can be collected across samples or time points, the workflow supports comparisons between conditions and examination of biological change.
Quantitative results can connect visible anatomical patterns with tissue composition and physiological function. In biological research, these measurements may help relate regional imaging changes to cellular processes, developmental events, or pathological conditions. The analysis does not replace biological interpretation, but it provides reproducible measurements that allow researchers to evaluate differences and track outcomes systematically.